Bottom Line:
In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation.Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices.Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

ABSTRACTTo what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

Mentions:
As transcription factors (TF) play an important role in the regulation of gene expression, we compared TF co-regulation between the different flux coupling types. We quantified the overlap in TFs upstream of operon pairs (TF similarity) as the number of shared TFs relative to the total number of involved TFs (Methods). As intra-operonic gene pairs show co-regulation by definition, we specifically studied those gene pairs that are encoded in different operons (i.e., inter-operonic) and are controlled by at least one known TF. Moreover, only those operon pairs were selected that contain inter-operonic gene pairs with the same type of flux coupling. We found that fully coupled operon pairs have, on average, higher TF similarities compared to both directionally and uncoupled ones (Wilcox multiple pairwise comparison, p < 10−2, Figure 4). Uncoupled operon pairs show extremely low TF similarities, which confirm the expectation that it would be irrelevant to co-regulate genes without a functional association.

Mentions:
As transcription factors (TF) play an important role in the regulation of gene expression, we compared TF co-regulation between the different flux coupling types. We quantified the overlap in TFs upstream of operon pairs (TF similarity) as the number of shared TFs relative to the total number of involved TFs (Methods). As intra-operonic gene pairs show co-regulation by definition, we specifically studied those gene pairs that are encoded in different operons (i.e., inter-operonic) and are controlled by at least one known TF. Moreover, only those operon pairs were selected that contain inter-operonic gene pairs with the same type of flux coupling. We found that fully coupled operon pairs have, on average, higher TF similarities compared to both directionally and uncoupled ones (Wilcox multiple pairwise comparison, p < 10−2, Figure 4). Uncoupled operon pairs show extremely low TF similarities, which confirm the expectation that it would be irrelevant to co-regulate genes without a functional association.

Bottom Line:
In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation.Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices.Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

ABSTRACTTo what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.